1. Field of the Invention
The present invention relates to a rhythm matching parallel processing apparatus and its computer program capable of, in a synchronization system of rhythm features obtained from music data and rhythm features obtained from motion capture data (hereinafter referred to as “MoCap”), parallelizing rhythm matching processing for automatic extraction of correlation values of the features to shorten computation time in correlation comparison of the features.
2. Description of the Related Art
The above-mentioned music synchronization system of the MoCap data aims to generate, for example, much or various dance performance matching a music from an input music signal. Typically, as illustrated in FIG. 4, there are processing (step S1) of matching between beat features of MoCap data and each segment of the music and extracting a plurality of motion segment candidates from the MoCap data for each segment of the music, processing (step S2) of checking connectability for the motion segment candidates to determine, out of the motion segment candidates, motion segment candidate pairs that can bring about natural motion when connected, and processing (step S3) of matching with swell features and outputting a pair of motion segment candidates of highest correlation. The step S1 can be called “rhythm matching processing”, the step S2 can be called “connectability analyzing processing”, and the step S3 can be called “swell matching processing”.
Incidentally, the conventional technique relating to the music synchronization system of the MoCap data is disclosed in, for example, the following non-patent documents 1 and 2.
The non-patent document 1 discloses the technique of analyzing music background relating to the expression of dance from an input music signal and generating dance performance in accordance with the analysis result, using dance performance of which expression is one important factor. Extracted from motion data are motion rhythm and swell features, and extracted from music data are rhythm and swell as features divided into segments by music structure analysis. In generation of the dance performance, first, motion segment candidates are all extracted that show high correlation with rhythm components in the music segments obtained by the structure analysis. Then, correlation of a last swell component is obtained to select optimal motion segments and connect them so that the dance performance is generated.
Besides, the non-patent document 2 discloses the technique of analyzing dance performance data obtained by motion capture by use of time-series correlation matrix. The time-series correlation matrix obtained from two motion data pieces is analyzed to inspect relation between the motions. First, attention is given to a feature in the time-series correlation matrix that shows when the two motions are similar to each other, and the motion similarity between the two data pieces is treated quantitatively. This makes it possible to automatically extract a motion area corresponding to a particular part of choreography from the motion data. Next, this similarity analysis is performed on motion data of two performers who perform the same dance thereby to be able to detect a difference between the performers' motions, that is, mistakes or habits.    Non-patent document 1: “Automatic Synthesis of Dance Performance Using Motion and Musical Features” (The transactions of the Institute of Electrons, Information and Communication Engineers (IEICE), D: Information and Systems, Vol. 90, No. 8, (2007/8), pp. 2242 to 2252)    Non-patent document 2: “Analysis of Dance Motion by Correlations between Motion Data” (The transactions of the Institute of Electrons, Information and Communication Engineers (IEICE), D-II: Information and Systems, Vol. J88-D-II, No. 8 (20050811), pp. 1652 to 1661)
In the conventional art, a large amount of MoCap data is required to automatically generate motion data of high correlation with music data. Therefore, in the case of increase in music data size or kinds of MoCap data, the number of pattern matching times and the number of computations of correlation values are extremely increased. Therefore, such generation of the motion data is difficult to realize in a PC, a portable phone or the like of low specifications.